Traditional machine learning metrics(TMLMs)are quite useful for the current research work precision,recall,accuracy,MSE and RMSE.Not enough for a practitioner to be confident about the performance and dependability of...Traditional machine learning metrics(TMLMs)are quite useful for the current research work precision,recall,accuracy,MSE and RMSE.Not enough for a practitioner to be confident about the performance and dependability of innovative interpretable model 85%–92%.We included in the prediction process,machine learning models(MLMs)with greater than 99%accuracy with a sensitivity of 95%–98%and specifically in the database.We need to explain the model to domain specialists through the MLMs.Human-understandable explanations in addition to ML professionals must establish trust in the prediction of our model.This is achieved by creating a model-independent,locally accurate explanation set that makes it better than the primary model.As we know that human interaction with machine learning systems on this model’s interpretability is more crucial.For supporting set validations in model selection insurance premium prediction.In this study,we proposed the use of the(LIME and SHAP)approach to understand research properly and explain a model developed using random forest regression to predict insurance premiums.The SHAP algorithm’s drawback,as seen in our experiments,is its lengthy computing time—to produce the findings,it must compute every possible combination.In addition,the experiments conducted were intended to focus on the model’s interpretability and explain its ability using LIME and SHAP,not the insurance premium charge prediction.Three experiments were conducted through experiment,one was to interpret the random forest regression model using LIME techniques.In experiment 2,we used the SHAP technique to interpret the model insurance premium prediction(IPP).展开更多
Agricultural insurance is a key impetus for agricultural modernization. How- ever, there are some problems on the aspect, including deficiency of law supports and top system design, less recognition on insurance of fa...Agricultural insurance is a key impetus for agricultural modernization. How- ever, there are some problems on the aspect, including deficiency of law supports and top system design, less recognition on insurance of farmers and low effective demands of agricultural insurance market, huge agricultural risks and insufficient supply from agricultural insurance market, and shortage of reinsurance support and disaster risk desertification, hardly to deal with heavy disasters. Therefore, some countermeasures were proposed, including to formulate agricultural insurance laws and establish specific agricultural insurance management institutions, to reinforce promotion and improve premium subsidy system in order to increase market de- mands, to increase tax preference and operating costs, improve insurance services and enhance effective supply in market and to construct a risk diversification system of agricultural heavy disaster.展开更多
Open-access gridded climate products have been suggested as a potential source of data for index insurance design and operation in data-limited regions.However,index insurance requires climate data with long historica...Open-access gridded climate products have been suggested as a potential source of data for index insurance design and operation in data-limited regions.However,index insurance requires climate data with long historical records,global geographical coverage and fine spatial resolution at the same time,which is nearly impossible to satisfy,especially with open-access data.In this paper,we spatially downscaled gridded climate data(precipitation,temperature,and soil moisture)in coarse spatial resolution with globally available longterm historical records to finer spatial resolution,using satellite-based data and machine learning algorithms.We then investigated the effect of index insurance contracts based on downscaled climate data for hedging spring wheat yield.This study employed countylevel spring wheat yield data between 1982 and 2018 from 56 counties overall in Kazakhstan and Mongolia.The results showed that in the majority of cases(70%),hedging effectiveness of index insurances increases when climate data is spatially downscaled with a machine learning approach.These improvements are statistically significant(p≤0.05).Among other climate data,more improvements in hedging effectiveness were observed when the insurance design was based on downscaled temperature and precipitation data.Overall,this study highlights the reasonability and benefits of downscaling climate data for insurance design and operation.展开更多
文摘Traditional machine learning metrics(TMLMs)are quite useful for the current research work precision,recall,accuracy,MSE and RMSE.Not enough for a practitioner to be confident about the performance and dependability of innovative interpretable model 85%–92%.We included in the prediction process,machine learning models(MLMs)with greater than 99%accuracy with a sensitivity of 95%–98%and specifically in the database.We need to explain the model to domain specialists through the MLMs.Human-understandable explanations in addition to ML professionals must establish trust in the prediction of our model.This is achieved by creating a model-independent,locally accurate explanation set that makes it better than the primary model.As we know that human interaction with machine learning systems on this model’s interpretability is more crucial.For supporting set validations in model selection insurance premium prediction.In this study,we proposed the use of the(LIME and SHAP)approach to understand research properly and explain a model developed using random forest regression to predict insurance premiums.The SHAP algorithm’s drawback,as seen in our experiments,is its lengthy computing time—to produce the findings,it must compute every possible combination.In addition,the experiments conducted were intended to focus on the model’s interpretability and explain its ability using LIME and SHAP,not the insurance premium charge prediction.Three experiments were conducted through experiment,one was to interpret the random forest regression model using LIME techniques.In experiment 2,we used the SHAP technique to interpret the model insurance premium prediction(IPP).
基金Supported by Humanities and Social Sciences Project of Education Department of Henan Provincial Government(2014-QN-276)Project of Education Department of Henan Provincial Government(2013-GH-261)Scientific Research Foundation for the Youth of Xinyang Normal University(2010013)~~
文摘Agricultural insurance is a key impetus for agricultural modernization. How- ever, there are some problems on the aspect, including deficiency of law supports and top system design, less recognition on insurance of farmers and low effective demands of agricultural insurance market, huge agricultural risks and insufficient supply from agricultural insurance market, and shortage of reinsurance support and disaster risk desertification, hardly to deal with heavy disasters. Therefore, some countermeasures were proposed, including to formulate agricultural insurance laws and establish specific agricultural insurance management institutions, to reinforce promotion and improve premium subsidy system in order to increase market de- mands, to increase tax preference and operating costs, improve insurance services and enhance effective supply in market and to construct a risk diversification system of agricultural heavy disaster.
基金supported by the German Federal Ministry of Education and Research(BMBF)[FKZ 01LZ1705A].
文摘Open-access gridded climate products have been suggested as a potential source of data for index insurance design and operation in data-limited regions.However,index insurance requires climate data with long historical records,global geographical coverage and fine spatial resolution at the same time,which is nearly impossible to satisfy,especially with open-access data.In this paper,we spatially downscaled gridded climate data(precipitation,temperature,and soil moisture)in coarse spatial resolution with globally available longterm historical records to finer spatial resolution,using satellite-based data and machine learning algorithms.We then investigated the effect of index insurance contracts based on downscaled climate data for hedging spring wheat yield.This study employed countylevel spring wheat yield data between 1982 and 2018 from 56 counties overall in Kazakhstan and Mongolia.The results showed that in the majority of cases(70%),hedging effectiveness of index insurances increases when climate data is spatially downscaled with a machine learning approach.These improvements are statistically significant(p≤0.05).Among other climate data,more improvements in hedging effectiveness were observed when the insurance design was based on downscaled temperature and precipitation data.Overall,this study highlights the reasonability and benefits of downscaling climate data for insurance design and operation.
